Abstract

The present study assesses the potential of the U.S. Climate Variability and Predictability (CLIVAR) Drought Working Group (DWG) models in simulating interannual precipitation variability over North America, especially the Great Plains. It also provides targets for the idealized DWG model experiments investigating drought origin. The century-long Atmospheric Model Intercomparison Project (AMIP) simulations produced by version 3.5 of NCAR’s Community Atmosphere Model (CAM3.5), the Lamont-Doherty Earth Observatory’s Community Climate Model (CCM3), and NASA’s Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric models are analyzed; CCM3 and NSIPP-1 models have 16- and 14-ensemble simulations, respectively, while CAM3.5 only has 1.

The standard deviation of summer precipitation is different in AMIP simulations. The maximum over the central United States seen in observations is placed farther to the west in simulations. Over the central plains the models exhibit modest skill in simulating low-frequency precipitation variability, a Palmer drought severity index proxy. The presence of a linear trend increases correlations in the period 1950–99 when compared with those for the whole century. The SST links of the Great Plains drought index have features in common with observations over both the Pacific and Atlantic Oceans.

Interestingly, summer-to-fall precipitation regressions of the warm Trend, cold Pacific, and warm Atlantic modes of annual mean SST variability (used in forcing the DWG idealized model experiments) tend to dry the southwestern, midwestern, and southeastern regions of the United States in the observations and, to a lesser extent, in the simulations.

The similarity of the idealized SST-forced droughts in DWG modeling experiments with AMIP precipitation regressions of the corresponding SST principal components, evident especially in the case of the cold Pacific pattern, suggests that the routinely conducted AMIP simulations could have served as an effective proxy for the more elaborated suite of DWG modeling experiments.

Abstract

The present work assesses spring and summer precipitation over North America as well as summer precipitation variability over the central United States and its SST links in simulations of the twentieth-century climate and projections of the twenty-first- and twenty-second-century climates for the A1B scenario.

The observed spatial structure of spring and summer precipitation poses a challenge for models, particularly over the western and central United States. Tendencies in spring precipitation in the twenty-first century agree with the observed ones at the end of the twentieth century over a wetter north-central and a drier southwestern United States, and a drier southeastern Mexico. Projected wetter springs over the Great Plains in the twenty-first and twenty-second centuries are associated with an increase in the number of extreme springs. In contrast, projected summer tendencies have demonstrated little consistency. The associated observed changes in SSTs bear the global warming footprint, which is not well captured in the twentieth-century climate simulations.

Precipitation variability over the Great Plains presents a coherent picture in spring but not in summer. Models project an increase in springtime precipitation variability owing to an increased number of extreme springs. The number of extreme droughty (pluvial) events during the spring–fall part of the year is under(over)estimated in the twentieth century without consistent projections.

Summer precipitation variability over the Great Plains is linked to SSTs over the Pacific and Atlantic Oceans, with no apparent ENSO link in spite of the exaggerated variability in the equatorial Pacific in climate simulations; this has been identified already in observations and atmospheric models forced with historical SSTs. This link is concealed due to the increased warming in the twenty-first century. Deficiencies in land surface–atmosphere interactions and global teleconnections in the climate models prevent them from a better portrayal of summer precipitation variability in the central United States.

Abstract

The annual cycle of precipitation and the interannual variability of the North American hydroclimate during summer months are analyzed in coupled simulations of the twentieth-century climate. The state-of-the-art general circulation models, participating in the Fourth Assessment Report for the Intergovernmental Panel on Climate Change (IPCC), included in the present study are the U.S. Community Climate System Model version 3 (CCSM3), the Parallel Climate Model (PCM), the Goddard Institute for Space Studies model version EH (GISS-EH), and the Geophysical Fluid Dynamics Laboratory Coupled Model version 2.1 (GFDL-CM2.1); the Met Office’s Third Hadley Centre Coupled Ocean–Atmosphere GCM (UKMO-HadCM3); and the Japanese Model for Interdisciplinary Research on Climate version 3.2 [MIROC3.2(hires)]. Datasets with proven high quality such as NCEP’s North American Regional Reanalysis (NARR), and the Climate Prediction Center (CPC) U.S.–Mexico precipitation analysis are used as targets for simulations.

Climatological precipitation is not easily simulated. While models capture winter precipitation very well over the U.S. northwest, they encounter failure over the U.S. southeast in the same season. Summer precipitation over the central United States and Mexico is also a great challenge for models, particularly the timing. In general the UKMO-HadCM3 is closest to the observations.

The models’ potential in simulating interannual hydroclimate variability over North America during the warm season is varied and limited to the central United States. Models like PCM, and in particular UKMO-HadCM3, exhibit reasonably well the observed distribution and relative importance of remote and local contributions to precipitation variability over the region (i.e., convergence of remote moisture fluxes dominate over local evapotranspiration). However, in models like CCSM3 and GFDL-CM2.1 local contributions dominate over remote ones, in contrast with warm-season observations. In the other extreme are models like GISS-EH and MIROC3.2(hires) that prioritize the remote influence of moisture fluxes and neglect the local influence of land surface processes to the regional precipitation variability.

Abstract

Interannual variability of warm-season rainfall over the Great Plains is analyzed using the recently released North American Regional Reanalysis (NARR). The new dataset differs from its global counterparts in the additional assimilation of precipitation and radiances. This along with the use of a more comprehensive land surface model in generation of NARR offers the prospect of obtaining improved estimates of surface hydrologic and near-surface meteorological fields.

NARR’s representation of hydroclimate is used to weigh in on the authors’ recent finding of the dominance of large-scale moisture flux convergence over evaporation in accounting for Great Plains precipitation variations. Evaporation estimates are notoriously uncertain and, while the NARR ones are not assured to be realistic, they are more constrained than those diagnosed before from inline and offline assessments.

NARR’s portrayal of warm-season hydroclimate variability corroborates the importance of remote water sources in generation of Great Plains precipitation variability and supports the authors’ claim that some state-of-the-art atmosphere/land surface models vigorously recycle precipitation, erroneously, at least in context of Great Plains interannual variability. These very models have been key to recent claims of strong coupling between soil moisture and precipitation.

Abstract

The monotony of seasonal variability is often compensated by the complexity of its spatial structure—the case in North American hydroclimate. The structure of hydroclimate variability is analyzed to provide insights into the functioning of the climate system and climate models.

The consistency of hydroclimate representation in two global [40-yr ECMWF Re-Analysis (ERA-40) and NCEP] and one regional [North American Regional Reanalysis (NARR)] reanalysis is examined first, from analysis of precipitation, evaporation, surface air temperature (SAT), and moisture flux distributions. The intercomparisons benchmark the recently released NARR data and provide context for evaluation of the simulation potential of two state-of-the-art atmospheric models [NCAR's Community Atmospheric Model (CAM3.0) and NASA's Seasonal-to-Interannual Prediction Project (NSIPP) atmospheric model].

Intercomparisons paint a gloomy picture: great divergence in global reanalysis representations of precipitation, with the eastern United States being drier in ERA-40 and wetter in NCEP in the annual mean by up to a third in each case; model averages are like ERA-40. The annual means, in fact, mask even larger but offsetting seasonal departures.

Analysis of moisture transport shows winter fluxes to be more consistently represented. Summer flux convergence over the Gulf Coast and Great Plains, however, differs considerably between global and regional reanalyses. Flux distributions help in understanding the choice of rainy season, especially the winter one in the Pacific Northwest; stationary fluxes are key.

Land–ocean competition for convection is too intense in the models—so much so that the oceanic ITCZ in July is southward of its winter position in the both simulations! The overresponsiveness of land is also manifest in SAT; the winter-to-summer change over the Great Plains is 5–9 K larger than in observations, with implications for modeling of climate sensitivity.

The nature of atmospheric water balance over the Great Plains is probed, despite unbalanced moisture budgets in reanalyses and model simulations. The imbalance is smaller in NARR but still unacceptably large, resulting from excessive evaporation in spring and summer. Adjusting evaporation during precipitation assimilation could lead to a more balanced budget.

Global and regional reanalysis will remain of limited use for hydroclimate studies until they comply with the operative water and energy balance constraints.

Abstract

Interannual variability of Great Plains precipitation in the warm season months is analyzed using gridded observations, satellite-based precipitation estimates, NCEP reanalysis data and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data, and the half-century-long NCAR Community Atmosphere Model (CAM3.0, version 3.0) and the National Aeronautics and Space Administration (NASA) Seasonal-to-Intraseasonal Prediction Project (NSIPP) atmospheric model simulations. Regional hydroclimate is the focus because of its immense societal impact and because the involved variability mechanisms are not well understood.

The Great Plains precipitation variability is represented rather differently, and only quasi realistically, in the reanalyses. NCEP has larger amplitude but less traction with observations in comparison with ERA-40. Model simulations exhibit more realistic amplitudes, which are between those of NCEP and ERA-40. The simulated variability is however uncorrelated with observations in both models, with monthly correlations smaller than 0.10 in all cases. An assessment of the regional atmosphere water balance is revealing: Stationary moisture flux convergence accounts for most of the Great Plains variability in ERA-40, but not in the NCEP reanalysis and model simulations; convergent fluxes generate less than half of the precipitation in the latter, while local evaporation does the rest in models.

Phenomenal evaporation in the models—up to 4 times larger than the highest observationally constrained estimate (NCEP’s)—provides the bulk of the moisture for Great Plains precipitation variability; thus, precipitation recycling is very efficient in both models, perhaps too efficient.

Remote water sources contribute substantially to Great Plains hydroclimate variability in nature via fluxes. Getting the interaction pathways right is presently challenging for the models.

Abstract

The Department of Energy Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site data are analyzed to provide insight into atmosphere–land surface interactions generating summertime precipitation variability. Pentad-averaged (5 days) data are analyzed; the average is long enough to suppress synoptic variability but sufficiently short to resolve atmosphere–land surface interactions. Intercomparison with the precipitation-assimilating North American Regional Reanalysis (NARR) helps with in-depth investigation of the processes. The analysis seeks to ascertain the process sequence, especially the role of evapotranspiration and soil-moisture–radiation feedbacks in the generation of regional precipitation variability at this temporal scale.

Transported moisture dominates over evapotranspiration in precipitation variability over the region, from both magnitude of the contribution to regional water balance and its apparent temporal lead at pentad resolution. Antecedent and contemporaneous evapotranspiration are found to be negatively correlated with precipitation, albeit statistically insignificant; only lagging correlations are positive, peaking at 2-pentad lag following precipitation, substantiating the authors’ characterization of the water balance over SGP, and extending the authors’ previous findings on the dominance of moisture flux convergence in generating precipitation variability at monthly scales.

Precipitation episodes are linked with net negative surface radiation anomalies (i.e., with an energy-deprived land surface state that cannot fuel evapotranspiration), ruling out radiatively driven positive feedback on precipitation. Although the net longwave signal is positive because of a colder land surface (less upward terrestrial radiation), it is more than offset by the cloudiness-related reduction in downward shortwave radiation. Thus, ARM (NARR) data do not support the soil-moisture–precipitation feedback hypothesis over the SGP at pentad time scales; however, it may work at subpentad resolution and over other regions.

Abstract

The Mekong River is the lifeblood of the Southeast (SE) Asian economies. In situ and satellite-based precipitation are analyzed to assess the amount of water received as precipitation in the river basin (Mekong basin water), in particular, the amount each country receives. Laos, Thailand, and Cambodia contribute ~75% of the basin water during March–September, whereas China’s contribution is 10%–15%, except in winter when it rises to 25%. The processing of Mekong basin water into Mekong streamflow entails accounting for the uncertain water losses but, interestingly, interannual variations in Mekong basin water can be processed into Mekong streamflow using a simple hydrologic model, which is validated using monthly river discharge data from four stations. Preliminary evidence for the impact of upbasin dams on downstream flow, especially the timing of peak summer flow, is presented. Characterization of El Niño’s influence on SE Asian rainfall reveals significant rainfall reductions in the fall preceding and the spring following El Niño’s peak phase (winter); such reductions at the bookends of the dry season in SE Asia (winter) generate droughts, as in 2015–16. The linear trend in twentieth-century rainfall assesses the vulnerability of the region to climate change. The analysis indicates the feasibility of streamflow prediction using a simple hydrologic model driven by high-resolution precipitation observations and forecasts. It raises the prospects of drought prediction based on El Niño’s emergence/forecast. Finally, by showing the Mekong to be largely a rain-fed and not snowmelt-fed river, it provides quantitative context for assessing the notion of Chinese control on the lower Mekong via upbasin dams.

Abstract

A search for coupled modes of atmosphere–ocean interaction in the tropical Atlantic sector is presented. Previous studies have provided conflicting indications of the existence of coupled modes in this region. The subject is revisited through a rotated principal component analysis performed on datasets spanning the 36-yr period 1958–93. The analysis includes four variables, sea surface temperature, oceanic heat content, wind stress, and atmospheric diabatic heating. The authors find that the first rotated principal component is associated with fluctuations in the subtropical wind system and correlates with the North Atlantic oscillation (NAO), while the second and third modes, which are the focus of interest, are related to tropical variability.

The second mode is the Atlantic Niño mode with anomalous sea surface temperature and anomalous heat content in the eastern equatorial basin. Wind stress weakens to the west of anomalously warm water, while convection is shifted south and eastward. Surface and upper-level wind anomalies of this mode resemble those of El Niño–Southern Oscillation (ENSO) events. When the analysis is limited to boreal summer, the season of maximum amplitude, the Atlantic Niño mode explains 7.5% of the variance of the five variables. Thermodynamic air–sea interactions do not seem to play a role for this mode.

The third mode is associated with an interhemispheric gradient of anomalous sea surface temperature and a dipole pattern of atmospheric heating. In its positive phase anomalous heating occurs over the warmer Northern Hemisphere with divergence aloft shifting convection to the north and west of the equator and intensifying the subtropical jet stream, while descending motion occurs on the western side of the Southern Hemisphere. Surface and subsurface structures in the ocean are controlled by surface winds. This interhemispheric mode is strongest in boreal spring when it explains 9.1% of the combined variance of the five variables. Thermodynamic air–sea interactions do seem to control the associated sea surface temperature anomalies, although equatorial dynamics may play a role as well.

The authors also examine the connection of the tropical Atlantic to other basins. ENSO events cause patterns of winds, heating, and sea surface temperatures resembling the interhemispheric mode described above. The lag between changes in the Atlantic and Pacific is 4–5 months for the interhemispheric mode. In contrast, no significant impact of ENSO is found on the Atlantic Niño mode. Likewise, no impact of the midlatitude North Atlantic (the NAO) is found on the Tropics, but some impact of the Tropics is found on the midlatitude North Atlantic.

Abstract

Due to the physical coupling between atmosphere and ocean, information about the ocean helps to better predict the future of the atmosphere, and in turn, information about the atmosphere helps to better predict the ocean. Here, we investigate the spatial and temporal nature of this predictability: where, for how long, and at what frequencies does the ocean significantly improve prediction of the atmosphere, and vice versa? We apply Granger causality, a statistical test to measure whether a variable improves prediction of another, to local time series of sea surface temperature (SST) and low-level atmospheric variables. We calculate the detailed spatial structure of the atmosphere-to-ocean and ocean-to-atmosphere predictability. We find that the atmosphere improves prediction of the ocean most in the extratropics, especially in regions of large SST gradients. This atmosphere-to-ocean predictability is weaker but longer-lived in the tropics, where it can last for several months in some regions. On the other hand, the ocean improves prediction of the atmosphere most significantly in the tropics, where this predictability lasts for months to over a year. However, we find a robust signature of the ocean on the atmosphere almost everywhere in the extratropics, an influence that has been difficult to demonstrate with model studies. We find that both the atmosphere-to-ocean and ocean-to-atmosphere predictability are maximal at low frequencies, and both are larger in the summer hemisphere. The patterns we observe generally agree with dynamical understanding and the results of the Kalnay dynamical rule, which diagnoses the direction of forcing between the atmosphere and ocean by considering the local phase relationship between simultaneous sea surface temperature and vorticity anomaly signals. We discuss applications to coupled data assimilation.